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Abstract-We present a planner for addressing a difficult, yet under-investigated class of planning problems: Fully Observable Non-Deterministic planning problems with strong solutions. Our strong planner employs a new data structure, MRDAG (multi-root directed acyclic graph), to define how the solution space should be expanded. We further equip a MRDAG with heuristics to ensure planning towards the relevant search direction. We performed extensive experiments to evaluate MRDAG and the heuristics. Results show that our strong algorithm achieves impressive performance on a variety of benchmark problems: on average it runs more than three orders of magnitude faster than the state-of-the-art planners, MBP and Gamer, and demonstrates significantly better scalability.

We present a planner for addressing a difficult, yet under-investigated class of planning problems: Fully Observable Non-Deterministic planning problems with strong solutions. Our strong planner employs a new data structure, MRDAG (multi-root directed acyclic graph), to define how the solution space should be expanded. We further equip a MRDAG with heuristics to ensure planning towards the relevant search direction. We performed extensive experiments to evaluate MRDAG and the heuristics. Results show that our strong algorithm achieves impressive performance on a variety of benchmark problems: on average it runs more than three orders of magnitude faster than the state-of-the-art planners, MBP and Gamer, and demonstrates significantly better scalability.

No abstract